Snapdocs has more than 500 employees and is the mortgage industry’s leading digital closing platform. Powering millions of closings each year, the company combines an open platform, patented AI technology, the largest settlement network, and a team of industry experts to ensure digital closing success.
Summary
Searchlight helped Snapdocs research the attributes, knowledge, skills, and experience that their best employees possess, and created predictive talent models that could improve Quality of Hire and reduce time to productivity.
Key Wins
• Improved Quality of Hire and increased Employee LifetimeValue by 1.5-4X
• Decreased Time to Productivity by 25%
• Gathered and analyzed talent data 80% faster (and in less than 4 weeks), surfacing patterns and trends in the attributes and experience that predict “successful employees”
"After using Searchlight, the Customer Success hires we made post-Searchlight were by far the strongest people we’ve hired in the 2 years we’ve recruited for our team. This success has created demand for Searchlight in other departments across the business."
Chad Herring
VP People, Snapdocs
Background
In 2021, Snapdocs’ Chief People Officer Chad Herring embarked on a talent analytics project to research the attributes, knowledge, skills, and experience that their best employees possess.
Challenge
Given how critical top talent is to the success of the business, this project had buy-in across the business. His prior experience leading similar initiatives convinced him how data helps organizations predictably attract and retain top performers. It also taught him that the work required, if done manually, exceeded his team’s capacity. His need for fast impact without compromising quality led him to partner with Searchlight.
Solution
Chad and the Snapdocs HR and Talent team segmented a yearlong engagement with Searchlight into 2 parts. Part 1 was the research phase, whereSearchlight analyzed Snapdocs current employees for three roles, differentiated between High Performers(“HPs”) and Non-High Performers (“NHPs”), and surfaced patterns and trends in the attributes and experience that predict HPs. The research collection and analysis took less than 4 weeks to complete and focused on Searchlight’s four dimensions ofBehavioral Data: Competencies, Strengths & Gaps,Cultural Alignment and Career Interests.
For each person, Searchlight created a talent profile representing their unique fingerprint and conducted data analysis using various regression models and statistical techniques to surface the most high-signal characteristics that differentiated the talent profiles of high performers versus the rest of the team. This data was then used to create Predictive Talent Models.
As an example, the resulting Predictive TalentModel for Snapdocs’ Customer Success Manager role identified the following HP characteristics:fast learner (HP frequency over NHPs = 2.6x),results-driven (2.2x), resourcefulness (2.2x), strategic thinking (2.2x), and intuition (1.9x).
Part 2 was the implementation phase whereSearchlight and Snapdocs worked together to useSearchlight’s predictive talent models and software pre-hire to hire candidates that aligned with the talent profile. After the Predictive Talent Model was complete, the scorecard with attributes that were most correlated to high performance was added into Searchlight’s Intake form. Recruiters and hiring managers were trained to assess candidates with this new scorecard, and Searchlight’s Reference Assessments were calibrated to match candidates to it.
With this data-driven approach, Snapdocs was able toapply greater focus and precision to find candidates that more closely fit elements of the Talent Model, for example which Target Power Skills to look for. To measure the business impact of the Predictive TalentModels at Snapdocs, the new revised scorecards were compared to prior hires in the same role (for example with the Customer Success Manager).
